| """Causal online feature builders shared by Phase 2 tabular baselines.""" |
|
|
| from __future__ import annotations |
|
|
| import hashlib |
| import json |
|
|
| import numpy as np |
|
|
|
|
| COMPACT_V1_WINDOWS = (3, 5, 10, 20, 50, 100) |
| EXPANDED_V2_LAGS = (1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610) |
| EXPANDED_V2_EWMA_ALPHAS = (0.02, 0.05, 0.10, 0.20, 0.40) |
| EXPANDED_V2_WINDOWS = (3, 5, 10, 20, 50, 100, 200, 500) |
|
|
|
|
| def _validate_history(history: np.ndarray) -> np.ndarray: |
| history = np.asarray(history, dtype=np.float32) |
| if history.ndim != 2 or history.shape[1] != 32: |
| raise ValueError(f"Expected history shape (t, 32), got {history.shape}") |
| if len(history) == 0: |
| raise ValueError("history must contain at least one state") |
| return history |
|
|
|
|
| def _ewma(history: np.ndarray, alpha: float) -> np.ndarray: |
| state = history[0].astype(np.float32, copy=True) |
| for row in history[1:]: |
| state = alpha * row + (1.0 - alpha) * state |
| return state.astype(np.float32) |
|
|
|
|
| def build_compact_v1_features(history: np.ndarray) -> np.ndarray: |
| """Build the original compact raw-32 causal feature vector.""" |
| history = _validate_history(history) |
| current = history[-1] |
| prev = history[-2] if len(history) >= 2 else np.zeros_like(current) |
| delta1 = current - prev |
|
|
| features = [current, delta1] |
| for window in COMPACT_V1_WINDOWS: |
| chunk = history[-window:] |
| mean = chunk.mean(axis=0) |
| features.extend([mean, chunk.std(axis=0), current - mean]) |
| return np.concatenate(features).astype(np.float32) |
|
|
|
|
| def build_expanded_v2_features(history: np.ndarray) -> np.ndarray: |
| """Build the Phase 2 max-safe expanded causal feature vector.""" |
| history = _validate_history(history) |
| current = history[-1] |
| features = [current] |
|
|
| for lag in EXPANDED_V2_LAGS: |
| lagged = history[-1 - lag] if len(history) > lag else history[0] |
| features.extend([lagged, current - lagged]) |
|
|
| for alpha in EXPANDED_V2_EWMA_ALPHAS: |
| smoothed = _ewma(history, alpha) |
| features.extend([smoothed, current - smoothed]) |
|
|
| for window in EXPANDED_V2_WINDOWS: |
| chunk = history[-window:] |
| mean = chunk.mean(axis=0) |
| features.extend( |
| [ |
| mean, |
| chunk.std(axis=0), |
| chunk.min(axis=0), |
| chunk.max(axis=0), |
| current - mean, |
| ] |
| ) |
| return np.concatenate(features).astype(np.float32) |
|
|
|
|
| def build_causal_tabular_features(history: np.ndarray, schema: str = "compact_v1") -> np.ndarray: |
| if schema == "compact_v1": |
| return build_compact_v1_features(history) |
| if schema == "expanded_v2": |
| return build_expanded_v2_features(history) |
| raise ValueError(f"Unknown feature schema: {schema}") |
|
|
|
|
| def feature_width(schema: str) -> int: |
| return int(build_causal_tabular_features(np.zeros((1, 32), dtype=np.float32), schema).shape[0]) |
|
|
|
|
| def infer_feature_schema_from_width(width: int) -> str: |
| width = int(width) |
| for schema in ("compact_v1", "expanded_v2"): |
| if feature_width(schema) == width: |
| return schema |
| raise ValueError(f"No known causal feature schema has width {width}") |
|
|
|
|
| def feature_schema_payload(schema: str) -> dict: |
| if schema == "compact_v1": |
| return { |
| "schema": schema, |
| "windows": list(COMPACT_V1_WINDOWS), |
| "width": feature_width(schema), |
| } |
| if schema == "expanded_v2": |
| return { |
| "schema": schema, |
| "lags": list(EXPANDED_V2_LAGS), |
| "ewma_alphas": list(EXPANDED_V2_EWMA_ALPHAS), |
| "windows": list(EXPANDED_V2_WINDOWS), |
| "width": feature_width(schema), |
| } |
| raise ValueError(f"Unknown feature schema: {schema}") |
|
|
|
|
| def feature_schema_hash(schema: str) -> str: |
| payload = json.dumps(feature_schema_payload(schema), sort_keys=True).encode("utf-8") |
| return hashlib.sha256(payload).hexdigest() |
|
|
